---
title: "OptimumDocumentEmbedder"
id: optimumdocumentembedder
slug: "/optimumdocumentembedder"
description: "A component to compute documents’ embeddings using models loaded with the Hugging Face Optimum library."
---

# OptimumDocumentEmbedder

A component to compute documents’ embeddings using models loaded with the Hugging Face Optimum library.

<div className="key-value-table">

|  |  |
| --- | --- |
| **Most common position in a pipeline** | Before a [`DocumentWriter`](../writers/documentwriter.mdx)  in an indexing pipeline                |
| **Mandatory run variables**            | `documents`: A list of documents                                                          |
| **Output variables**                   | `documents`: A list of documents enriched with embeddings                                 |
| **API reference**                      | [Optimum](/reference/integrations-optimum)                                                       |
| **GitHub link**                        | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/optimum |

</div>

## Overview

`OptimumDocumentEmbedder` embeds text strings using models loaded with the [HuggingFace Optimum](https://huggingface.co/docs/optimum/index) library. It uses the [ONNX runtime](https://onnxruntime.ai/) for high-speed inference.

The default model is `sentence-transformers/all-mpnet-base-v2`.

Similarly to other Embedders, this component allows adding prefixes (and suffixes) to include instructions. For more details, refer to the component’s API reference.

There are three useful parameters specific to the Optimum Embedder that you can control with various modes:

- [Pooling](/reference/integrations-optimum#optimumembedderpooling): generate a fixed-sized sentence embedding from a variable-sized sentence embedding
- [Optimization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization): apply graph optimization to the model and improve inference speed
- [Quantization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization): reduce the computational and memory costs

Find all the available mode details in our Optimum [API Reference](/reference/integrations-optimum).

### Authentication

Authentication with a Hugging Face API Token is only required to access private or gated models through Serverless Inference API or the Inference Endpoints.

The component uses an `HF_API_TOKEN` or `HF_TOKEN` environment variable, or you can pass a Hugging Face API token at initialization. See our [Secret Management](../../concepts/secret-management.mdx) page for more information.

## Usage

To start using this integration with Haystack, install it with:

```shell
pip install optimum-haystack
```

### On its own

```python
from haystack.dataclasses import Document
from haystack_integrations.components.embedders.optimum import OptimumDocumentEmbedder

doc = Document(content="I love pizza!")

document_embedder = OptimumDocumentEmbedder(model="sentence-transformers/all-mpnet-base-v2")
document_embedder.warm_up()

result = document_embedder.run([doc])
print(result["documents"][0].embedding)

## [0.017020374536514282, -0.023255806416273117, ...]
```

### In a pipeline

```python
from haystack import Pipeline
from haystack import Document
from haystack_integrations.components.embedders.optimum import (
    OptimumDocumentEmbedder,
    OptimumEmbedderPooling,
    OptimumEmbedderOptimizationConfig,
    OptimumEmbedderOptimizationMode,
)

documents = [
    Document(content="My name is Wolfgang and I live in Berlin"),
    Document(content="I saw a black horse running"),
    Document(content="Germany has many big cities"),
]

embedder = OptimumDocumentEmbedder(
    model="intfloat/e5-base-v2",
    normalize_embeddings=True,
    onnx_execution_provider="CUDAExecutionProvider",
    optimizer_settings=OptimumEmbedderOptimizationConfig(
        mode=OptimumEmbedderOptimizationMode.O4,
        for_gpu=True,
    ),
    working_dir="/tmp/optimum",
    pooling_mode=OptimumEmbedderPooling.MEAN,
)

pipeline = Pipeline()
pipeline.add_component("embedder", embedder)

pipeline.run({"embedder": {"documents": documents}})

print(results["embedder"]["embedding"])
```
